A new adaptive threshold technique for improved matching in SIFT

Scale Invariant Feature Transform (SIFT) is widely used in vision systems for various applications such as object detection and face recognition. In SIFT, threshold is applied to determine local extrema (keypoint selection) and global extrema (keypoint refinement). Next, descriptor matching is performed with selected keypoints. This paper presents a new method of adaptive thresholding which improves keypoint selection in SIFT. The value of adaptive threshold depends upon the average regional intensity of an image. Experimental results show that our method is robust for matching the keypoints among the images with illumination differences. Our new adaptive threshold technique for keypoint selection reduces false matches and shows significantly improved performance in experimental results.